DRTriton: Large-Scale Synthetic Data Reinforcement Learning for Triton Kernel Generation
DRTriton: Large-Scale Synthetic Data Reinforcement Learning for Triton Kernel Generation explores A reinforcement learning framework that trains LLMs to generate highly optimized Triton kernels from PyTorch code, significantly outperforming existing models and human experts on kernel optimization tasks.. Commercial viability score: 7/10 in LLM Training.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisCUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel GenerationViability: 3.0
- Builds On ThisBeyond GEMM-Centric NPUs: Enabling Efficient Diffusion LLM SamplingViability: 2.0
- Prior WorkKernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement LearningViability: 7.0
- Prior WorkAn Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPUViability: 7.0
- Prior WorkCuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe